Exploring AI-Based Support in Speech-Language Pathology for Culturally and Linguistically Diverse Children

要旨

Speech-language pathologists (SLPs) provide support to children with speech and language difficulties through delivering evaluation, assessment, and interventions. Despite growing research on how Artificial Intelligence (AI) can support SLPs, there is limited research examining how AI can assist SLPs in delivering equitable care to culturally and linguistically diverse (CLD) children with disabilities. Through interviews with 15 SLPs and a two-part survey study with 13 SLPs, we report on SLP challenges in delivering responsive care to CLD children with disabilities (i.e., unrepresentative materials, unreliable translation, insufficient support for language variations), areas for AI-based support, evaluations of how available AI performs in addressing these challenges, and bias assessments of AI-generated materials. We discuss implications of contextually unaware AI, the range of care in AI-prompting, tensions and tradeoffs of AI-based support, and honoring diverse representations in AI-generated materials. We offer considerations for SLPs using AI-based tools and general-purpose AI in their practice.

著者
Aaleyah Lewis
University of Washington, Seattle, Washington, United States
Aayushi Dangol
University of Washington, SEATTLE, Washington, United States
Hyewon Suh
University of Washington, Seattle, Washington, United States
Abbie Olszewski
University of Nevada, Reno, Reno, Nevada, United States
James Fogarty
University of Washington, Seattle, Washington, United States
Julie A.. Kientz
University of Washington, Seattle, Washington, United States
DOI

10.1145/3706598.3714131

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714131

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Communication and Support

G402
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
日本語まとめ
読み込み中…